class_docstr: The MissingValues Bar visualizer creates a bar graph that lists the total
  count of missing values for each selected feature column.
  
  When y targets are supplied to fit, the output is a stacked bar chart where
  each color corresponds to the total NaNs for the feature in that column.
  
  Parameters
  ----------
  alpha : float, default: 0.5
      A value for bending elments with the background.
  
  marker : matplotlib marker, default: |
      The marker used for each element coordinate in the plot
  
  color : string, default: black
      The color for drawing the bar chart when the y targets are not passed to
      fit.
  
  colors : list, default: None
      The color palette for drawing a stack bar chart when the y targets
      are passed to fit.
  
  classes : list, default: None
      A list of class names for the legend.
      If classes is None and a y value is passed to fit then the classes
      are selected from the target vector.
  
  kwargs : dict
      Keyword arguments that are passed to the base class and may influence
      the visualization as defined in other Visualizers.
  
  Attributes
  ----------
  features_ : np.array
      The feature labels ranked according to their importance
  
  classes_ : np.array
      The class labels for each of the target values
  
  Examples
  --------
  
  >>> from yellowbrick.contrib.missing import MissingValuesBar
  >>> visualizer = MissingValuesBar()
  >>> visualizer.fit(X, y=y)
  >>> visualizer.show()
docstr: Sets a title and x-axis labels and adds a legend. Also ensures that the
  y tick values are correctly set to feature names.
  
  Parameters
  ----------
  kwargs: generic keyword arguments.
  
  Notes
  -----
  Generally this method is called from show and not directly by the user.
docstr_tok: ["Sets","a","title","and","x-axis","labels","and","adds","a","legend",".","Also","ensures","that","the","y","tick","values","are","correctly","set","to","feature","names",".","Parameters","--","--","--","--","--","kwargs",":","generic","keyword","arguments",".","Notes","--","--","-","Generally","this","method","is","called","from","show","and","not","directly","by","the","user","."]
code: def finalize(self, **kwargs):
      """
      Sets a title and x-axis labels and adds a legend. Also ensures that the
      y tick values are correctly set to feature names.
  
      Parameters
      ----------
      kwargs: generic keyword arguments.
  
      Notes
      -----
      Generally this method is called from show and not directly by the user.
      """
      # Set the title
      self.set_title("Count of Missing Values by Column")
      tick_locations = np.arange(
          len(self.features_)
      )  # the x locations for the groups
      self.ax.set_yticks(tick_locations)
      self.ax.set_yticklabels(self.get_feature_names())
      # Remove the ticks from the graph
      self.ax.set_xlabel("Count")
  
      self.ax.legend(loc="best")
  
file_path: yellowbrick/yellowbrick/contrib/missing/bar.py
imports: import numpy
  from yellowbrick.style.palettes import color_palette
  from .base import MissingDataVisualizer
label_desc: Use image node_id 5 for calling the MissingValuesBar obj's underlying member method code with example usage: obj.finalize() without return types